Overview

Dataset statistics

Number of variables33
Number of observations11939
Missing cells12924
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 MiB
Average record size in memory926.7 B

Variable types

Numeric15
Categorical18

Warnings

country has a high cardinality: 109 distinct values High cardinality
reservation_status_date has a high cardinality: 852 distinct values High cardinality
df_index is highly correlated with agentHigh correlation
arrival_date_year is highly correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly correlated with arrival_date_yearHigh correlation
agent is highly correlated with df_indexHigh correlation
df_index is highly correlated with agentHigh correlation
arrival_date_year is highly correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly correlated with arrival_date_yearHigh correlation
is_repeated_guest is highly correlated with previous_bookings_not_canceledHigh correlation
previous_bookings_not_canceled is highly correlated with is_repeated_guestHigh correlation
agent is highly correlated with df_indexHigh correlation
is_repeated_guest is highly correlated with previous_bookings_not_canceledHigh correlation
previous_bookings_not_canceled is highly correlated with is_repeated_guestHigh correlation
arrival_date_week_number is highly correlated with arrival_date_month and 3 other fieldsHigh correlation
hotel is highly correlated with agent and 3 other fieldsHigh correlation
is_canceled is highly correlated with df_index and 1 other fieldsHigh correlation
arrival_date_month is highly correlated with arrival_date_week_number and 1 other fieldsHigh correlation
agent is highly correlated with hotel and 2 other fieldsHigh correlation
stays_in_weekend_nights is highly correlated with stays_in_week_nightsHigh correlation
df_index is highly correlated with arrival_date_week_number and 6 other fieldsHigh correlation
lead_time is highly correlated with companyHigh correlation
assigned_room_type is highly correlated with hotel and 2 other fieldsHigh correlation
deposit_type is highly correlated with reservation_statusHigh correlation
reservation_status is highly correlated with is_canceled and 2 other fieldsHigh correlation
stays_in_week_nights is highly correlated with stays_in_weekend_nights and 1 other fieldsHigh correlation
company is highly correlated with arrival_date_week_number and 6 other fieldsHigh correlation
arrival_date_year is highly correlated with arrival_date_week_number and 2 other fieldsHigh correlation
previous_cancellations is highly correlated with previous_bookings_not_canceledHigh correlation
market_segment is highly correlated with company and 1 other fieldsHigh correlation
reserved_room_type is highly correlated with assigned_room_type and 1 other fieldsHigh correlation
previous_bookings_not_canceled is highly correlated with previous_cancellationsHigh correlation
distribution_channel is highly correlated with market_segmentHigh correlation
children is highly correlated with assigned_room_type and 1 other fieldsHigh correlation
market_segment is highly correlated with distribution_channelHigh correlation
assigned_room_type is highly correlated with reserved_room_typeHigh correlation
reservation_status is highly correlated with is_canceledHigh correlation
reserved_room_type is highly correlated with assigned_room_typeHigh correlation
is_canceled is highly correlated with reservation_statusHigh correlation
distribution_channel is highly correlated with market_segmentHigh correlation
agent has 1635 (13.7%) missing values Missing
company has 11241 (94.2%) missing values Missing
adults is highly skewed (γ1 = 20.76322339) Skewed
previous_cancellations is highly skewed (γ1 = 23.75659833) Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 21.99555117) Skewed
df_index has unique values Unique
lead_time has 635 (5.3%) zeros Zeros
stays_in_weekend_nights has 5276 (44.2%) zeros Zeros
stays_in_week_nights has 771 (6.5%) zeros Zeros
previous_cancellations has 11325 (94.9%) zeros Zeros
previous_bookings_not_canceled has 11560 (96.8%) zeros Zeros
booking_changes has 10139 (84.9%) zeros Zeros
days_in_waiting_list has 11561 (96.8%) zeros Zeros
adr has 204 (1.7%) zeros Zeros
total_of_special_requests has 7020 (58.8%) zeros Zeros

Reproduction

Analysis started2022-01-18 20:55:08.782011
Analysis finished2022-01-18 20:55:54.372008
Duration45.59 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct11939
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59706.35053
Minimum5
Maximum119388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:54.436397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5590.4
Q129931.5
median59416
Q389234
95-th percentile113493.1
Maximum119388
Range119383
Interquartile range (IQR)59302.5

Descriptive statistics

Standard deviation34480.59589
Coefficient of variation (CV)0.5775029888
Kurtosis-1.191425583
Mean59706.35053
Median Absolute Deviation (MAD)29642
Skewness0.002508635686
Sum712834119
Variance1188911493
MonotonicityNot monotonic
2022-01-18T21:55:54.552350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225251
 
< 0.1%
1183441
 
< 0.1%
874001
 
< 0.1%
607751
 
< 0.1%
95741
 
< 0.1%
689631
 
< 0.1%
381661
 
< 0.1%
1003381
 
< 0.1%
710081
 
< 0.1%
1026701
 
< 0.1%
Other values (11929)11929
99.9%
ValueCountFrequency (%)
51
< 0.1%
91
< 0.1%
241
< 0.1%
621
< 0.1%
711
< 0.1%
741
< 0.1%
781
< 0.1%
801
< 0.1%
841
< 0.1%
851
< 0.1%
ValueCountFrequency (%)
1193881
< 0.1%
1193621
< 0.1%
1193611
< 0.1%
1193441
< 0.1%
1193211
< 0.1%
1193051
< 0.1%
1193021
< 0.1%
1192981
< 0.1%
1192941
< 0.1%
1192921
< 0.1%

hotel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size789.1 KiB
City Hotel
7931 
Resort Hotel
4008 

Length

Max length12
Median length10
Mean length10.67141302
Min length10

Characters and Unicode

Total characters127406
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity Hotel
2nd rowResort Hotel
3rd rowCity Hotel
4th rowCity Hotel
5th rowCity Hotel

Common Values

ValueCountFrequency (%)
City Hotel7931
66.4%
Resort Hotel4008
33.6%

Length

2022-01-18T21:55:54.780232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:54.855231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
hotel11939
50.0%
city7931
33.2%
resort4008
 
16.8%

Most occurring characters

ValueCountFrequency (%)
t23878
18.7%
o15947
12.5%
e15947
12.5%
11939
9.4%
H11939
9.4%
l11939
9.4%
C7931
 
6.2%
i7931
 
6.2%
y7931
 
6.2%
R4008
 
3.1%
Other values (2)8016
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter91589
71.9%
Uppercase Letter23878
 
18.7%
Space Separator11939
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t23878
26.1%
o15947
17.4%
e15947
17.4%
l11939
13.0%
i7931
 
8.7%
y7931
 
8.7%
s4008
 
4.4%
r4008
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
H11939
50.0%
C7931
33.2%
R4008
 
16.8%
Space Separator
ValueCountFrequency (%)
11939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin115467
90.6%
Common11939
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t23878
20.7%
o15947
13.8%
e15947
13.8%
H11939
10.3%
l11939
10.3%
C7931
 
6.9%
i7931
 
6.9%
y7931
 
6.9%
R4008
 
3.5%
s4008
 
3.5%
Common
ValueCountFrequency (%)
11939
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII127406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t23878
18.7%
o15947
12.5%
e15947
12.5%
11939
9.4%
H11939
9.4%
l11939
9.4%
C7931
 
6.2%
i7931
 
6.2%
y7931
 
6.2%
R4008
 
3.1%
Other values (2)8016
 
6.3%

is_canceled
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
0
7526 
1
4413 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11939
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
07526
63.0%
14413
37.0%

Length

2022-01-18T21:55:55.010635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:55.071677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
07526
63.0%
14413
37.0%

Most occurring characters

ValueCountFrequency (%)
07526
63.0%
14413
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11939
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07526
63.0%
14413
37.0%

Most occurring scripts

ValueCountFrequency (%)
Common11939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07526
63.0%
14413
37.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07526
63.0%
14413
37.0%

lead_time
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct445
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.2650976
Minimum0
Maximum629
Zeros635
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:55.150686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median67
Q3159
95-th percentile316
Maximum629
Range629
Interquartile range (IQR)142

Descriptive statistics

Standard deviation105.9764844
Coefficient of variation (CV)1.036291823
Kurtosis1.799286001
Mean102.2650976
Median Absolute Deviation (MAD)58
Skewness1.371569719
Sum1220943
Variance11231.01524
MonotonicityNot monotonic
2022-01-18T21:55:55.259594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0635
 
5.3%
1347
 
2.9%
2216
 
1.8%
3175
 
1.5%
5165
 
1.4%
4150
 
1.3%
6145
 
1.2%
7142
 
1.2%
8116
 
1.0%
10115
 
1.0%
Other values (435)9733
81.5%
ValueCountFrequency (%)
0635
5.3%
1347
2.9%
2216
 
1.8%
3175
 
1.5%
4150
 
1.3%
5165
 
1.4%
6145
 
1.2%
7142
 
1.2%
8116
 
1.0%
9110
 
0.9%
ValueCountFrequency (%)
6293
< 0.1%
6263
< 0.1%
6224
< 0.1%
6152
< 0.1%
6082
< 0.1%
6053
< 0.1%
6011
 
< 0.1%
5941
 
< 0.1%
5873
< 0.1%
5801
 
< 0.1%

arrival_date_year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
2016
5697 
2017
4081 
2015
2161 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters47756
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2016
3rd row2015
4th row2017
5th row2016

Common Values

ValueCountFrequency (%)
20165697
47.7%
20174081
34.2%
20152161
 
18.1%

Length

2022-01-18T21:55:55.460706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:55.518706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
20165697
47.7%
20174081
34.2%
20152161
 
18.1%

Most occurring characters

ValueCountFrequency (%)
211939
25.0%
011939
25.0%
111939
25.0%
65697
11.9%
74081
 
8.5%
52161
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number47756
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
211939
25.0%
011939
25.0%
111939
25.0%
65697
11.9%
74081
 
8.5%
52161
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common47756
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
211939
25.0%
011939
25.0%
111939
25.0%
65697
11.9%
74081
 
8.5%
52161
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII47756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
211939
25.0%
011939
25.0%
111939
25.0%
65697
11.9%
74081
 
8.5%
52161
 
4.5%

arrival_date_month
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size733.4 KiB
August
1346 
July
1249 
May
1223 
April
1117 
October
1115 
Other values (7)
5889 

Length

Max length9
Median length6
Mean length5.8947148
Min length3

Characters and Unicode

Total characters70377
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNovember
2nd rowApril
3rd rowDecember
4th rowJanuary
5th rowApril

Common Values

ValueCountFrequency (%)
August1346
11.3%
July1249
10.5%
May1223
10.2%
April1117
9.4%
October1115
9.3%
June1087
9.1%
September995
8.3%
March965
8.1%
February835
7.0%
December711
6.0%
Other values (2)1296
10.9%

Length

2022-01-18T21:55:55.709706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august1346
11.3%
july1249
10.5%
may1223
10.2%
april1117
9.4%
october1115
9.3%
june1087
9.1%
september995
8.3%
march965
8.1%
february835
7.0%
december711
6.0%
Other values (2)1296
10.9%

Most occurring characters

ValueCountFrequency (%)
e9511
13.5%
r7869
 
11.2%
u6481
 
9.2%
b4334
 
6.2%
a4259
 
6.1%
y3925
 
5.6%
t3456
 
4.9%
J2954
 
4.2%
c2791
 
4.0%
A2463
 
3.5%
Other values (16)22334
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter58438
83.0%
Uppercase Letter11939
 
17.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9511
16.3%
r7869
13.5%
u6481
11.1%
b4334
 
7.4%
a4259
 
7.3%
y3925
 
6.7%
t3456
 
5.9%
c2791
 
4.8%
m2384
 
4.1%
l2366
 
4.0%
Other values (8)11062
18.9%
Uppercase Letter
ValueCountFrequency (%)
J2954
24.7%
A2463
20.6%
M2188
18.3%
O1115
 
9.3%
S995
 
8.3%
F835
 
7.0%
D711
 
6.0%
N678
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin70377
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9511
13.5%
r7869
 
11.2%
u6481
 
9.2%
b4334
 
6.2%
a4259
 
6.1%
y3925
 
5.6%
t3456
 
4.9%
J2954
 
4.2%
c2791
 
4.0%
A2463
 
3.5%
Other values (16)22334
31.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII70377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e9511
13.5%
r7869
 
11.2%
u6481
 
9.2%
b4334
 
6.2%
a4259
 
6.1%
y3925
 
5.6%
t3456
 
4.9%
J2954
 
4.2%
c2791
 
4.0%
A2463
 
3.5%
Other values (16)22334
31.7%

arrival_date_week_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.06868247
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:55.803706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median27
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.70172554
Coefficient of variation (CV)0.5061836885
Kurtosis-0.993153523
Mean27.06868247
Median Absolute Deviation (MAD)11
Skewness0.009828044899
Sum323173
Variance187.7372827
MonotonicityNot monotonic
2022-01-18T21:55:55.910923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33340
 
2.8%
30317
 
2.7%
20316
 
2.6%
25291
 
2.4%
34290
 
2.4%
32288
 
2.4%
29287
 
2.4%
17286
 
2.4%
18286
 
2.4%
41275
 
2.3%
Other values (43)8963
75.1%
ValueCountFrequency (%)
1106
0.9%
2126
1.1%
3124
1.0%
4175
1.5%
5136
1.1%
6157
1.3%
7214
1.8%
8245
2.1%
9211
1.8%
10189
1.6%
ValueCountFrequency (%)
53211
1.8%
52115
1.0%
5192
0.8%
50147
1.2%
49187
1.6%
48156
1.3%
47150
1.3%
46161
1.3%
45203
1.7%
44223
1.9%

arrival_date_day_of_month
Real number (ℝ≥0)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.97470475
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:56.011424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.742890086
Coefficient of variation (CV)0.5472958795
Kurtosis-1.173654697
Mean15.97470475
Median Absolute Deviation (MAD)8
Skewness-0.02684347686
Sum190722
Variance76.43812706
MonotonicityNot monotonic
2022-01-18T21:55:56.101287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17468
 
3.9%
15453
 
3.8%
24443
 
3.7%
5432
 
3.6%
12424
 
3.6%
20420
 
3.5%
19416
 
3.5%
28414
 
3.5%
27410
 
3.4%
25409
 
3.4%
Other values (21)7650
64.1%
ValueCountFrequency (%)
1363
3.0%
2363
3.0%
3392
3.3%
4342
2.9%
5432
3.6%
6350
2.9%
7362
3.0%
8386
3.2%
9401
3.4%
10371
3.1%
ValueCountFrequency (%)
31219
1.8%
30388
3.2%
29370
3.1%
28414
3.5%
27410
3.4%
26385
3.2%
25409
3.4%
24443
3.7%
23367
3.1%
22360
3.0%

stays_in_weekend_nights
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9141469135
Minimum0
Maximum9
Zeros5276
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:56.197472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.987058179
Coefficient of variation (CV)1.079758805
Kurtosis3.016416053
Mean0.9141469135
Median Absolute Deviation (MAD)1
Skewness1.143543888
Sum10914
Variance0.9742838487
MonotonicityNot monotonic
2022-01-18T21:55:56.274480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
05276
44.2%
23273
27.4%
13047
25.5%
4175
 
1.5%
3135
 
1.1%
618
 
0.2%
89
 
0.1%
54
 
< 0.1%
71
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
05276
44.2%
13047
25.5%
23273
27.4%
3135
 
1.1%
4175
 
1.5%
54
 
< 0.1%
618
 
0.2%
71
 
< 0.1%
89
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
89
 
0.1%
71
 
< 0.1%
618
 
0.2%
54
 
< 0.1%
4175
 
1.5%
3135
 
1.1%
23273
27.4%
13047
25.5%
05276
44.2%

stays_in_week_nights
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.487729291
Minimum0
Maximum22
Zeros771
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:56.366595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum22
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.869170221
Coefficient of variation (CV)0.7513559566
Kurtosis13.4165766
Mean2.487729291
Median Absolute Deviation (MAD)1
Skewness2.401236979
Sum29701
Variance3.493797315
MonotonicityNot monotonic
2022-01-18T21:55:56.451601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
23434
28.8%
12997
25.1%
32230
18.7%
51081
 
9.1%
4948
 
7.9%
0771
 
6.5%
6139
 
1.2%
7114
 
1.0%
1088
 
0.7%
873
 
0.6%
Other values (11)64
 
0.5%
ValueCountFrequency (%)
0771
 
6.5%
12997
25.1%
23434
28.8%
32230
18.7%
4948
 
7.9%
51081
 
9.1%
6139
 
1.2%
7114
 
1.0%
873
 
0.6%
923
 
0.2%
ValueCountFrequency (%)
222
 
< 0.1%
214
 
< 0.1%
204
 
< 0.1%
193
 
< 0.1%
161
 
< 0.1%
1512
0.1%
144
 
< 0.1%
133
 
< 0.1%
122
 
< 0.1%
116
0.1%

adults
Real number (ℝ≥0)

SKEWED

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.853840355
Minimum0
Maximum40
Zeros42
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:56.536591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6048854144
Coefficient of variation (CV)0.3262877587
Kurtosis1324.471113
Mean1.853840355
Median Absolute Deviation (MAD)0
Skewness20.76322339
Sum22133
Variance0.3658863645
MonotonicityNot monotonic
2022-01-18T21:55:56.616582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
28911
74.6%
12346
 
19.6%
3632
 
5.3%
042
 
0.4%
46
 
0.1%
51
 
< 0.1%
401
 
< 0.1%
ValueCountFrequency (%)
042
 
0.4%
12346
 
19.6%
28911
74.6%
3632
 
5.3%
46
 
0.1%
51
 
< 0.1%
401
 
< 0.1%
ValueCountFrequency (%)
401
 
< 0.1%
51
 
< 0.1%
46
 
0.1%
3632
 
5.3%
28911
74.6%
12346
 
19.6%
042
 
0.4%

children
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
0.0
11101 
1.0
 
468
2.0
 
365
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters35817
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.011101
93.0%
1.0468
 
3.9%
2.0365
 
3.1%
3.05
 
< 0.1%

Length

2022-01-18T21:55:56.819587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:56.883591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.011101
93.0%
1.0468
 
3.9%
2.0365
 
3.1%
3.05
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
023040
64.3%
.11939
33.3%
1468
 
1.3%
2365
 
1.0%
35
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23878
66.7%
Other Punctuation11939
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023040
96.5%
1468
 
2.0%
2365
 
1.5%
35
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.11939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35817
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023040
64.3%
.11939
33.3%
1468
 
1.3%
2365
 
1.0%
35
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII35817
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023040
64.3%
.11939
33.3%
1468
 
1.3%
2365
 
1.0%
35
 
< 0.1%

babies
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
0
11832 
1
 
105
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11939
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011832
99.1%
1105
 
0.9%
22
 
< 0.1%

Length

2022-01-18T21:55:57.074532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:57.136403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
011832
99.1%
1105
 
0.9%
22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011832
99.1%
1105
 
0.9%
22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11939
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011832
99.1%
1105
 
0.9%
22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011832
99.1%
1105
 
0.9%
22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011832
99.1%
1105
 
0.9%
22
 
< 0.1%

meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size688.7 KiB
BB
9222 
HB
1446 
SC
1084 
Undefined
 
99
FB
 
88

Length

Max length9
Median length2
Mean length2.058045062
Min length2

Characters and Unicode

Total characters24571
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB9222
77.2%
HB1446
 
12.1%
SC1084
 
9.1%
Undefined99
 
0.8%
FB88
 
0.7%

Length

2022-01-18T21:55:57.323998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:57.394001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
bb9222
77.2%
hb1446
 
12.1%
sc1084
 
9.1%
undefined99
 
0.8%
fb88
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B19978
81.3%
H1446
 
5.9%
S1084
 
4.4%
C1084
 
4.4%
n198
 
0.8%
d198
 
0.8%
e198
 
0.8%
U99
 
0.4%
f99
 
0.4%
i99
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter23779
96.8%
Lowercase Letter792
 
3.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B19978
84.0%
H1446
 
6.1%
S1084
 
4.6%
C1084
 
4.6%
U99
 
0.4%
F88
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
n198
25.0%
d198
25.0%
e198
25.0%
f99
12.5%
i99
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin24571
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B19978
81.3%
H1446
 
5.9%
S1084
 
4.4%
C1084
 
4.4%
n198
 
0.8%
d198
 
0.8%
e198
 
0.8%
U99
 
0.4%
f99
 
0.4%
i99
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII24571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B19978
81.3%
H1446
 
5.9%
S1084
 
4.4%
C1084
 
4.4%
n198
 
0.8%
d198
 
0.8%
e198
 
0.8%
U99
 
0.4%
f99
 
0.4%
i99
 
0.4%

country
Categorical

HIGH CARDINALITY

Distinct109
Distinct (%)0.9%
Missing48
Missing (%)0.4%
Memory size698.2 KiB
PRT
4880 
GBR
1165 
FRA
1009 
ESP
866 
DEU
729 
Other values (104)
3242 

Length

Max length3
Median length3
Mean length2.989235556
Min length2

Characters and Unicode

Total characters35545
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.2%

Sample

1st rowFRA
2nd rowPRT
3rd rowFRA
4th rowGBR
5th rowFRA

Common Values

ValueCountFrequency (%)
PRT4880
40.9%
GBR1165
 
9.8%
FRA1009
 
8.5%
ESP866
 
7.3%
DEU729
 
6.1%
ITA404
 
3.4%
IRL323
 
2.7%
NLD223
 
1.9%
BRA222
 
1.9%
BEL213
 
1.8%
Other values (99)1857
 
15.6%

Length

2022-01-18T21:55:57.849907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt4880
41.0%
gbr1165
 
9.8%
fra1009
 
8.5%
esp866
 
7.3%
deu729
 
6.1%
ita404
 
3.4%
irl323
 
2.7%
nld223
 
1.9%
bra222
 
1.9%
bel213
 
1.8%
Other values (99)1857
 
15.6%

Most occurring characters

ValueCountFrequency (%)
R8033
22.6%
P5896
16.6%
T5485
15.4%
E2161
 
6.1%
A2135
 
6.0%
B1632
 
4.6%
S1410
 
4.0%
U1353
 
3.8%
G1262
 
3.6%
F1057
 
3.0%
Other values (16)5121
14.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter35545
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R8033
22.6%
P5896
16.6%
T5485
15.4%
E2161
 
6.1%
A2135
 
6.0%
B1632
 
4.6%
S1410
 
4.0%
U1353
 
3.8%
G1262
 
3.6%
F1057
 
3.0%
Other values (16)5121
14.4%

Most occurring scripts

ValueCountFrequency (%)
Latin35545
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R8033
22.6%
P5896
16.6%
T5485
15.4%
E2161
 
6.1%
A2135
 
6.0%
B1632
 
4.6%
S1410
 
4.0%
U1353
 
3.8%
G1262
 
3.6%
F1057
 
3.0%
Other values (16)5121
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII35545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R8033
22.6%
P5896
16.6%
T5485
15.4%
E2161
 
6.1%
A2135
 
6.0%
B1632
 
4.6%
S1410
 
4.0%
U1353
 
3.8%
G1262
 
3.6%
F1057
 
3.0%
Other values (16)5121
14.4%

market_segment
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size770.0 KiB
Online TA
5692 
Offline TA/TO
2410 
Groups
1957 
Direct
1235 
Corporate
 
542
Other values (2)
 
103

Length

Max length13
Median length9
Mean length9.027724265
Min length6

Characters and Unicode

Total characters107782
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowOnline TA
3rd rowOffline TA/TO
4th rowOnline TA
5th rowDirect

Common Values

ValueCountFrequency (%)
Online TA5692
47.7%
Offline TA/TO2410
20.2%
Groups1957
 
16.4%
Direct1235
 
10.3%
Corporate542
 
4.5%
Complementary74
 
0.6%
Aviation29
 
0.2%

Length

2022-01-18T21:55:58.041855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:58.114856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
online5692
28.4%
ta5692
28.4%
ta/to2410
12.0%
offline2410
12.0%
groups1957
 
9.8%
direct1235
 
6.2%
corporate542
 
2.7%
complementary74
 
0.4%
aviation29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n13897
12.9%
O10512
9.8%
T10512
9.8%
e10027
9.3%
i9395
8.7%
l8176
7.6%
A8131
7.5%
8102
7.5%
f4820
 
4.5%
r4350
 
4.0%
Other values (14)19860
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64307
59.7%
Uppercase Letter32963
30.6%
Space Separator8102
 
7.5%
Other Punctuation2410
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n13897
21.6%
e10027
15.6%
i9395
14.6%
l8176
12.7%
f4820
 
7.5%
r4350
 
6.8%
o3144
 
4.9%
p2573
 
4.0%
u1957
 
3.0%
s1957
 
3.0%
Other values (6)4011
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
O10512
31.9%
T10512
31.9%
A8131
24.7%
G1957
 
5.9%
D1235
 
3.7%
C616
 
1.9%
Space Separator
ValueCountFrequency (%)
8102
100.0%
Other Punctuation
ValueCountFrequency (%)
/2410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97270
90.2%
Common10512
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n13897
14.3%
O10512
10.8%
T10512
10.8%
e10027
10.3%
i9395
9.7%
l8176
8.4%
A8131
8.4%
f4820
 
5.0%
r4350
 
4.5%
o3144
 
3.2%
Other values (12)14306
14.7%
Common
ValueCountFrequency (%)
8102
77.1%
/2410
 
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII107782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n13897
12.9%
O10512
9.8%
T10512
9.8%
e10027
9.3%
i9395
8.7%
l8176
7.6%
A8131
7.5%
8102
7.5%
f4820
 
4.5%
r4350
 
4.0%
Other values (14)19860
18.4%

distribution_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size726.9 KiB
TA/TO
9809 
Direct
1461 
Corporate
 
652
GDS
 
17

Length

Max length9
Median length5
Mean length5.337968004
Min length3

Characters and Unicode

Total characters63730
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowTA/TO
3rd rowTA/TO
4th rowTA/TO
5th rowDirect

Common Values

ValueCountFrequency (%)
TA/TO9809
82.2%
Direct1461
 
12.2%
Corporate652
 
5.5%
GDS17
 
0.1%

Length

2022-01-18T21:55:58.320629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:58.391391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
ta/to9809
82.2%
direct1461
 
12.2%
corporate652
 
5.5%
gds17
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T19618
30.8%
A9809
15.4%
/9809
15.4%
O9809
15.4%
r2765
 
4.3%
e2113
 
3.3%
t2113
 
3.3%
D1478
 
2.3%
i1461
 
2.3%
c1461
 
2.3%
Other values (6)3294
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter41400
65.0%
Lowercase Letter12521
 
19.6%
Other Punctuation9809
 
15.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2765
22.1%
e2113
16.9%
t2113
16.9%
i1461
11.7%
c1461
11.7%
o1304
10.4%
p652
 
5.2%
a652
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
T19618
47.4%
A9809
23.7%
O9809
23.7%
D1478
 
3.6%
C652
 
1.6%
G17
 
< 0.1%
S17
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/9809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin53921
84.6%
Common9809
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T19618
36.4%
A9809
18.2%
O9809
18.2%
r2765
 
5.1%
e2113
 
3.9%
t2113
 
3.9%
D1478
 
2.7%
i1461
 
2.7%
c1461
 
2.7%
o1304
 
2.4%
Other values (5)1990
 
3.7%
Common
ValueCountFrequency (%)
/9809
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII63730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T19618
30.8%
A9809
15.4%
/9809
15.4%
O9809
15.4%
r2765
 
4.3%
e2113
 
3.3%
t2113
 
3.3%
D1478
 
2.3%
i1461
 
2.3%
c1461
 
2.3%
Other values (6)3294
 
5.2%

is_repeated_guest
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
0
11562 
1
 
377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11939
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011562
96.8%
1377
 
3.2%

Length

2022-01-18T21:55:58.563677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:58.621677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
011562
96.8%
1377
 
3.2%

Most occurring characters

ValueCountFrequency (%)
011562
96.8%
1377
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11939
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011562
96.8%
1377
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common11939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011562
96.8%
1377
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011562
96.8%
1377
 
3.2%

previous_cancellations
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08895217355
Minimum0
Maximum26
Zeros11325
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:58.687044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9283031604
Coefficient of variation (CV)10.43598063
Kurtosis617.4229725
Mean0.08895217355
Median Absolute Deviation (MAD)0
Skewness23.75659833
Sum1062
Variance0.8617467576
MonotonicityNot monotonic
2022-01-18T21:55:58.770043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
011325
94.9%
1568
 
4.8%
215
 
0.1%
36
 
0.1%
246
 
0.1%
265
 
< 0.1%
114
 
< 0.1%
63
 
< 0.1%
253
 
< 0.1%
42
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
011325
94.9%
1568
 
4.8%
215
 
0.1%
36
 
0.1%
42
 
< 0.1%
63
 
< 0.1%
114
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
246
 
0.1%
ValueCountFrequency (%)
265
 
< 0.1%
253
 
< 0.1%
246
 
0.1%
141
 
< 0.1%
131
 
< 0.1%
114
 
< 0.1%
63
 
< 0.1%
42
 
< 0.1%
36
 
0.1%
215
0.1%

previous_bookings_not_canceled
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.140882821
Minimum0
Maximum59
Zeros11560
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:58.864043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum59
Range59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.465840309
Coefficient of variation (CV)10.40467744
Kurtosis661.5111628
Mean0.140882821
Median Absolute Deviation (MAD)0
Skewness21.99555117
Sum1682
Variance2.148687812
MonotonicityNot monotonic
2022-01-18T21:55:58.952698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
011560
96.8%
1164
 
1.4%
257
 
0.5%
339
 
0.3%
418
 
0.2%
714
 
0.1%
514
 
0.1%
812
 
0.1%
612
 
0.1%
118
 
0.1%
Other values (19)41
 
0.3%
ValueCountFrequency (%)
011560
96.8%
1164
 
1.4%
257
 
0.5%
339
 
0.3%
418
 
0.2%
514
 
0.1%
612
 
0.1%
714
 
0.1%
812
 
0.1%
95
 
< 0.1%
ValueCountFrequency (%)
591
< 0.1%
581
< 0.1%
451
< 0.1%
441
< 0.1%
331
< 0.1%
311
< 0.1%
281
< 0.1%
252
< 0.1%
242
< 0.1%
212
< 0.1%

reserved_room_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size676.4 KiB
A
8612 
D
1933 
E
 
647
F
 
266
G
 
191
Other values (3)
 
290

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11939
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowE
3rd rowA
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
A8612
72.1%
D1933
 
16.2%
E647
 
5.4%
F266
 
2.2%
G191
 
1.6%
B124
 
1.0%
C111
 
0.9%
H55
 
0.5%

Length

2022-01-18T21:55:59.145386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:59.218376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a8612
72.1%
d1933
 
16.2%
e647
 
5.4%
f266
 
2.2%
g191
 
1.6%
b124
 
1.0%
c111
 
0.9%
h55
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A8612
72.1%
D1933
 
16.2%
E647
 
5.4%
F266
 
2.2%
G191
 
1.6%
B124
 
1.0%
C111
 
0.9%
H55
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11939
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A8612
72.1%
D1933
 
16.2%
E647
 
5.4%
F266
 
2.2%
G191
 
1.6%
B124
 
1.0%
C111
 
0.9%
H55
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin11939
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A8612
72.1%
D1933
 
16.2%
E647
 
5.4%
F266
 
2.2%
G191
 
1.6%
B124
 
1.0%
C111
 
0.9%
H55
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A8612
72.1%
D1933
 
16.2%
E647
 
5.4%
F266
 
2.2%
G191
 
1.6%
B124
 
1.0%
C111
 
0.9%
H55
 
0.5%

assigned_room_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size676.4 KiB
A
7419 
D
2555 
E
768 
F
 
354
C
 
250
Other values (5)
 
593

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11939
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowE
3rd rowA
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
A7419
62.1%
D2555
 
21.4%
E768
 
6.4%
F354
 
3.0%
C250
 
2.1%
G241
 
2.0%
B229
 
1.9%
H65
 
0.5%
I37
 
0.3%
K21
 
0.2%

Length

2022-01-18T21:55:59.549505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:55:59.639263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
a7419
62.1%
d2555
 
21.4%
e768
 
6.4%
f354
 
3.0%
c250
 
2.1%
g241
 
2.0%
b229
 
1.9%
h65
 
0.5%
i37
 
0.3%
k21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A7419
62.1%
D2555
 
21.4%
E768
 
6.4%
F354
 
3.0%
C250
 
2.1%
G241
 
2.0%
B229
 
1.9%
H65
 
0.5%
I37
 
0.3%
K21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11939
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A7419
62.1%
D2555
 
21.4%
E768
 
6.4%
F354
 
3.0%
C250
 
2.1%
G241
 
2.0%
B229
 
1.9%
H65
 
0.5%
I37
 
0.3%
K21
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin11939
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A7419
62.1%
D2555
 
21.4%
E768
 
6.4%
F354
 
3.0%
C250
 
2.1%
G241
 
2.0%
B229
 
1.9%
H65
 
0.5%
I37
 
0.3%
K21
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A7419
62.1%
D2555
 
21.4%
E768
 
6.4%
F354
 
3.0%
C250
 
2.1%
G241
 
2.0%
B229
 
1.9%
H65
 
0.5%
I37
 
0.3%
K21
 
0.2%

booking_changes
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2175224056
Minimum0
Maximum9
Zeros10139
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:55:59.800124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6183174707
Coefficient of variation (CV)2.842546124
Kurtosis27.64457801
Mean0.2175224056
Median Absolute Deviation (MAD)0
Skewness4.278352626
Sum2597
Variance0.3823164946
MonotonicityNot monotonic
2022-01-18T21:55:59.875133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
010139
84.9%
11271
 
10.6%
2371
 
3.1%
397
 
0.8%
436
 
0.3%
512
 
0.1%
67
 
0.1%
73
 
< 0.1%
92
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
010139
84.9%
11271
 
10.6%
2371
 
3.1%
397
 
0.8%
436
 
0.3%
512
 
0.1%
67
 
0.1%
73
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
81
 
< 0.1%
73
 
< 0.1%
67
 
0.1%
512
 
0.1%
436
 
0.3%
397
 
0.8%
2371
 
3.1%
11271
 
10.6%
010139
84.9%

deposit_type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.3 KiB
No Deposit
10482 
Non Refund
1437 
Refundable
 
20

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters119390
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit10482
87.8%
Non Refund1437
 
12.0%
Refundable20
 
0.2%

Length

2022-01-18T21:56:00.178657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:56:00.260571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
deposit10482
43.9%
no10482
43.9%
refund1437
 
6.0%
non1437
 
6.0%
refundable20
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o22401
18.8%
e11959
10.0%
N11919
10.0%
11919
10.0%
D10482
8.8%
p10482
8.8%
s10482
8.8%
i10482
8.8%
t10482
8.8%
n2894
 
2.4%
Other values (7)5888
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter83613
70.0%
Uppercase Letter23858
 
20.0%
Space Separator11919
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22401
26.8%
e11959
14.3%
p10482
12.5%
s10482
12.5%
i10482
12.5%
t10482
12.5%
n2894
 
3.5%
f1457
 
1.7%
u1457
 
1.7%
d1457
 
1.7%
Other values (3)60
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N11919
50.0%
D10482
43.9%
R1457
 
6.1%
Space Separator
ValueCountFrequency (%)
11919
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107471
90.0%
Common11919
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22401
20.8%
e11959
11.1%
N11919
11.1%
D10482
9.8%
p10482
9.8%
s10482
9.8%
i10482
9.8%
t10482
9.8%
n2894
 
2.7%
R1457
 
1.4%
Other values (6)4431
 
4.1%
Common
ValueCountFrequency (%)
11919
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII119390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22401
18.8%
e11959
10.0%
N11919
10.0%
11919
10.0%
D10482
8.8%
p10482
8.8%
s10482
8.8%
i10482
8.8%
t10482
8.8%
n2894
 
2.4%
Other values (7)5888
 
4.9%

agent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct223
Distinct (%)2.2%
Missing1635
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean86.44817547
Minimum1
Maximum531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:56:00.342594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median14
Q3229
95-th percentile250
Maximum531
Range530
Interquartile range (IQR)220

Descriptive statistics

Standard deviation110.432807
Coefficient of variation (CV)1.277445203
Kurtosis0.01658087489
Mean86.44817547
Median Absolute Deviation (MAD)12
Skewness1.095463857
Sum890762
Variance12195.40487
MonotonicityNot monotonic
2022-01-18T21:56:00.482671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93265
27.3%
2401392
11.7%
1664
 
5.6%
14344
 
2.9%
6328
 
2.7%
7320
 
2.7%
250299
 
2.5%
28176
 
1.5%
241170
 
1.4%
8157
 
1.3%
Other values (213)3189
26.7%
(Missing)1635
13.7%
ValueCountFrequency (%)
1664
 
5.6%
221
 
0.2%
3117
 
1.0%
47
 
0.1%
538
 
0.3%
6328
 
2.7%
7320
 
2.7%
8157
 
1.3%
93265
27.3%
1022
 
0.2%
ValueCountFrequency (%)
5318
0.1%
5273
 
< 0.1%
5261
 
< 0.1%
5091
 
< 0.1%
5022
 
< 0.1%
4956
0.1%
4934
< 0.1%
4924
< 0.1%
4801
 
< 0.1%
4793
 
< 0.1%

company
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct163
Distinct (%)23.4%
Missing11241
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean189
Minimum9
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:56:00.634485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile40
Q167
median178
Q3260
95-th percentile428.75
Maximum543
Range534
Interquartile range (IQR)193

Descriptive statistics

Standard deviation131.6184216
Coefficient of variation (CV)0.6963937649
Kurtosis-0.4128483484
Mean189
Median Absolute Deviation (MAD)103
Skewness0.6395076039
Sum131922
Variance17323.4089
MonotonicityNot monotonic
2022-01-18T21:56:00.756457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4091
 
0.8%
22381
 
0.7%
4528
 
0.2%
15326
 
0.2%
6725
 
0.2%
28118
 
0.2%
21914
 
0.1%
15414
 
0.1%
17413
 
0.1%
23312
 
0.1%
Other values (153)376
 
3.1%
(Missing)11241
94.2%
ValueCountFrequency (%)
93
< 0.1%
111
 
< 0.1%
122
 
< 0.1%
141
 
< 0.1%
161
 
< 0.1%
205
< 0.1%
282
 
< 0.1%
312
 
< 0.1%
383
< 0.1%
392
 
< 0.1%
ValueCountFrequency (%)
5431
 
< 0.1%
5311
 
< 0.1%
5281
 
< 0.1%
5252
 
< 0.1%
5211
 
< 0.1%
5141
 
< 0.1%
5121
 
< 0.1%
5061
 
< 0.1%
5041
 
< 0.1%
49811
0.1%

days_in_waiting_list
Real number (ℝ≥0)

ZEROS

Distinct87
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.492336042
Minimum0
Maximum391
Zeros11561
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:56:00.977863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.18989246
Coefficient of variation (CV)7.699560629
Kurtosis186.7271495
Mean2.492336042
Median Absolute Deviation (MAD)0
Skewness12.16410697
Sum29756
Variance368.2519726
MonotonicityNot monotonic
2022-01-18T21:56:01.143698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011561
96.8%
3939
 
0.3%
5823
 
0.2%
4413
 
0.1%
3111
 
0.1%
310
 
0.1%
6310
 
0.1%
6910
 
0.1%
629
 
0.1%
1879
 
0.1%
Other values (77)244
 
2.0%
ValueCountFrequency (%)
011561
96.8%
12
 
< 0.1%
310
 
0.1%
41
 
< 0.1%
63
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
141
 
< 0.1%
154
 
< 0.1%
177
 
0.1%
ValueCountFrequency (%)
3918
0.1%
3305
< 0.1%
2364
< 0.1%
2241
 
< 0.1%
2236
0.1%
2151
 
< 0.1%
2074
< 0.1%
1879
0.1%
1781
 
< 0.1%
1766
0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size783.8 KiB
Transient
8950 
Transient-Party
2521 
Contract
 
404
Group
 
64

Length

Max length15
Median length9
Mean length10.21165927
Min length5

Characters and Unicode

Total characters121917
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient8950
75.0%
Transient-Party2521
 
21.1%
Contract404
 
3.4%
Group64
 
0.5%

Length

2022-01-18T21:56:01.407015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:56:01.478980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
transient8950
75.0%
transient-party2521
 
21.1%
contract404
 
3.4%
group64
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n23346
19.1%
t14800
12.1%
r14460
11.9%
a14396
11.8%
T11471
9.4%
s11471
9.4%
i11471
9.4%
e11471
9.4%
-2521
 
2.1%
P2521
 
2.1%
Other values (7)3989
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter104936
86.1%
Uppercase Letter14460
 
11.9%
Dash Punctuation2521
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n23346
22.2%
t14800
14.1%
r14460
13.8%
a14396
13.7%
s11471
10.9%
i11471
10.9%
e11471
10.9%
y2521
 
2.4%
o468
 
0.4%
c404
 
0.4%
Other values (2)128
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T11471
79.3%
P2521
 
17.4%
C404
 
2.8%
G64
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
-2521
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119396
97.9%
Common2521
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n23346
19.6%
t14800
12.4%
r14460
12.1%
a14396
12.1%
T11471
9.6%
s11471
9.6%
i11471
9.6%
e11471
9.6%
P2521
 
2.1%
y2521
 
2.1%
Other values (6)1468
 
1.2%
Common
ValueCountFrequency (%)
-2521
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII121917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n23346
19.1%
t14800
12.1%
r14460
11.9%
a14396
11.8%
T11471
9.4%
s11471
9.4%
i11471
9.4%
e11471
9.4%
-2521
 
2.1%
P2521
 
2.1%
Other values (7)3989
 
3.3%

adr
Real number (ℝ≥0)

ZEROS

Distinct2440
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.8099422
Minimum0
Maximum392
Zeros204
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:56:01.593966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q169.43
median95
Q3126
95-th percentile192
Maximum392
Range392
Interquartile range (IQR)56.57

Descriptive statistics

Standard deviation48.36573863
Coefficient of variation (CV)0.4750590913
Kurtosis1.939955893
Mean101.8099422
Median Absolute Deviation (MAD)28
Skewness0.9767056269
Sum1215508.9
Variance2339.244673
MonotonicityNot monotonic
2022-01-18T21:56:01.733238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62332
 
2.8%
75280
 
2.3%
90229
 
1.9%
65222
 
1.9%
0204
 
1.7%
80195
 
1.6%
95179
 
1.5%
120164
 
1.4%
110153
 
1.3%
85150
 
1.3%
Other values (2430)9831
82.3%
ValueCountFrequency (%)
0204
1.7%
13
 
< 0.1%
1.561
 
< 0.1%
1.81
 
< 0.1%
22
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
68
 
0.1%
6.41
 
< 0.1%
6.671
 
< 0.1%
ValueCountFrequency (%)
3921
< 0.1%
3871
< 0.1%
3571
< 0.1%
349.631
< 0.1%
3431
< 0.1%
3402
< 0.1%
336.571
< 0.1%
3301
< 0.1%
329.671
< 0.1%
3282
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.4 KiB
0
11221 
1
 
715
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11939
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011221
94.0%
1715
 
6.0%
23
 
< 0.1%

Length

2022-01-18T21:56:02.009831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:56:02.088832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
011221
94.0%
1715
 
6.0%
23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011221
94.0%
1715
 
6.0%
23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11939
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011221
94.0%
1715
 
6.0%
23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011221
94.0%
1715
 
6.0%
23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011221
94.0%
1715
 
6.0%
23
 
< 0.1%

total_of_special_requests
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.569645699
Minimum0
Maximum5
Zeros7020
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size93.4 KiB
2022-01-18T21:56:02.170358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7871496621
Coefficient of variation (CV)1.381823234
Kurtosis1.5214115
Mean0.569645699
Median Absolute Deviation (MAD)0
Skewness1.340795689
Sum6801
Variance0.6196045905
MonotonicityNot monotonic
2022-01-18T21:56:02.277546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
07020
58.8%
13341
28.0%
21318
 
11.0%
3221
 
1.9%
434
 
0.3%
55
 
< 0.1%
ValueCountFrequency (%)
07020
58.8%
13341
28.0%
21318
 
11.0%
3221
 
1.9%
434
 
0.3%
55
 
< 0.1%
ValueCountFrequency (%)
55
 
< 0.1%
434
 
0.3%
3221
 
1.9%
21318
 
11.0%
13341
28.0%
07020
58.8%

reservation_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size765.2 KiB
Check-Out
7526 
Canceled
4292 
No-Show
 
121

Length

Max length9
Median length9
Mean length8.620236201
Min length7

Characters and Unicode

Total characters102917
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCanceled
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out7526
63.0%
Canceled4292
35.9%
No-Show121
 
1.0%

Length

2022-01-18T21:56:02.532932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-18T21:56:02.619016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
check-out7526
63.0%
canceled4292
35.9%
no-show121
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e16110
15.7%
C11818
11.5%
c11818
11.5%
h7647
7.4%
-7647
7.4%
k7526
7.3%
O7526
7.3%
u7526
7.3%
t7526
7.3%
a4292
 
4.2%
Other values (7)13481
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter75684
73.5%
Uppercase Letter19586
 
19.0%
Dash Punctuation7647
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e16110
21.3%
c11818
15.6%
h7647
10.1%
k7526
9.9%
u7526
9.9%
t7526
9.9%
a4292
 
5.7%
n4292
 
5.7%
l4292
 
5.7%
d4292
 
5.7%
Other values (2)363
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C11818
60.3%
O7526
38.4%
N121
 
0.6%
S121
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
-7647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin95270
92.6%
Common7647
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e16110
16.9%
C11818
12.4%
c11818
12.4%
h7647
8.0%
k7526
7.9%
O7526
7.9%
u7526
7.9%
t7526
7.9%
a4292
 
4.5%
n4292
 
4.5%
Other values (6)9189
9.6%
Common
ValueCountFrequency (%)
-7647
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII102917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e16110
15.7%
C11818
11.5%
c11818
11.5%
h7647
7.4%
-7647
7.4%
k7526
7.3%
O7526
7.3%
u7526
7.3%
t7526
7.3%
a4292
 
4.2%
Other values (7)13481
13.1%

reservation_status_date
Categorical

HIGH CARDINALITY

Distinct852
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size758.0 KiB
21-10-15
 
137
06-07-15
 
77
25-11-16
 
73
18-01-16
 
73
01-01-15
 
69
Other values (847)
11510 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters95512
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.2%

Sample

1st row30-11-15
2nd row25-04-16
3rd row03-01-16
4th row27-11-16
5th row27-04-16

Common Values

ValueCountFrequency (%)
21-10-15137
 
1.1%
06-07-1577
 
0.6%
25-11-1673
 
0.6%
18-01-1673
 
0.6%
01-01-1569
 
0.6%
07-12-1647
 
0.4%
02-07-1540
 
0.3%
31-01-1737
 
0.3%
15-03-1637
 
0.3%
09-02-1636
 
0.3%
Other values (842)11313
94.8%

Length

2022-01-18T21:56:02.870895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21-10-15137
 
1.1%
06-07-1577
 
0.6%
25-11-1673
 
0.6%
18-01-1673
 
0.6%
01-01-1569
 
0.6%
07-12-1647
 
0.4%
02-07-1540
 
0.3%
31-01-1737
 
0.3%
15-03-1637
 
0.3%
09-02-1636
 
0.3%
Other values (842)11313
94.8%

Most occurring characters

ValueCountFrequency (%)
-23878
25.0%
121879
22.9%
015069
15.8%
67888
 
8.3%
26894
 
7.2%
76063
 
6.3%
54722
 
4.9%
32648
 
2.8%
82317
 
2.4%
92127
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71634
75.0%
Dash Punctuation23878
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
121879
30.5%
015069
21.0%
67888
 
11.0%
26894
 
9.6%
76063
 
8.5%
54722
 
6.6%
32648
 
3.7%
82317
 
3.2%
92127
 
3.0%
42027
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
-23878
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common95512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-23878
25.0%
121879
22.9%
015069
15.8%
67888
 
8.3%
26894
 
7.2%
76063
 
6.3%
54722
 
4.9%
32648
 
2.8%
82317
 
2.4%
92127
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII95512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-23878
25.0%
121879
22.9%
015069
15.8%
67888
 
8.3%
26894
 
7.2%
76063
 
6.3%
54722
 
4.9%
32648
 
2.8%
82317
 
2.4%
92127
 
2.2%

Interactions

2022-01-18T21:55:16.044524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:16.260861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:16.426178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:16.658501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:16.821910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:17.023473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:17.193015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:17.422434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:17.614511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:17.796472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:17.943459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:18.150737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:18.353638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:18.543682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:18.736826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:18.916941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:19.121979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:19.397802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:19.588736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:19.763063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:19.935386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:20.094049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:20.271646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:20.441218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:20.642161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:20.841356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:21.020421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:21.218101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:21.422161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:21.648241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:21.815156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:22.024491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:22.198270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:22.373328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:22.558198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:22.709323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:22.903217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:23.083069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:23.294398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:23.475697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:23.662858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:23.806042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:24.006945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:24.214217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:24.399915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:24.576532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:24.768073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:24.942606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:25.101196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:25.278163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:25.475893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:25.767422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:25.934349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:26.085792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:26.285641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:26.443213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-18T21:55:26.600279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2022-01-18T21:56:02.987809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-18T21:56:03.305516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-18T21:56:03.701232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-18T21:56:04.045662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-01-18T21:56:04.424843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-01-18T21:55:51.921165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-18T21:55:53.630028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-18T21:55:53.986160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-18T21:55:54.144327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexhotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
080965City Hotel022015November48271220.00BBFRADirectDirect000DD0No Deposit14.0NaN0Transient85.0000Check-Out30-11-15
123416Resort Hotel0272016April17221220.00BBPRTOnline TATA/TO000EE0No Deposit240.0NaN0Transient85.0001Check-Out25-04-16
282624City Hotel0402015December53310320.00BBFRAOffline TA/TOTA/TO000AA0No Deposit104.0NaN0Transient118.0000Check-Out03-01-16
362104City Hotel11232017January121120.00BBGBROnline TATA/TO000DD0No Deposit9.0NaN0Transient102.6001Canceled27-11-16
487859City Hotel0682016April17232220.00BBFRADirectDirect000DD0No DepositNaNNaN0Transient102.8500Check-Out27-04-16
525646Resort Hotel01582016June27282520.00BBCHEDirectDirect000EF0No DepositNaNNaN0Transient131.5011Check-Out05-07-16
652161City Hotel11132016May23310310.00BBPRTCorporateTA/TO000AA0Non RefundNaN202.00Transient100.0000Canceled11-04-16
757010City Hotel12052016September38161220.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN0Transient120.0000Canceled15-03-16
843958City Hotel1882015September39262220.00BBPRTGroupsTA/TO000AA0Non Refund1.0NaN0Transient170.0000Canceled09-09-15
979032City Hotel002015October43200110.00BBITAOffline TA/TOTA/TO000AB1No Deposit87.0NaN0Transient123.0000Check-Out21-10-15

Last rows

df_indexhotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
1192966021City Hotel11062017April15130220.00SCESPOnline TATA/TO000AA0No Deposit9.0NaN0Transient108.0000Canceled01-01-17
1193062215City Hotel15452017January150220.00BBPRTGroupsTA/TO000AA0Non Refund1.0NaN0Transient62.0000Canceled21-10-15
1193120374Resort Hotel042016January5260110.00BBPRTCorporateCorporate000AD0No DepositNaNNaN0Transient38.0000Check-Out27-01-16
1193222464Resort Hotel0362016March13240330.00BBPRTOffline TA/TOTA/TO000DD0No Deposit314.0NaN0Transient81.0001Check-Out27-03-16
119336487Resort Hotel12212016June25123520.00BBPRTDirectDirect000GG0No Deposit250.0NaN0Transient114.7501Canceled09-11-15
1193477023City Hotel0142015September3610120.00BBPRTOnline TATA/TO000AD0No Deposit9.0NaN0Contract105.0002Check-Out02-09-15
1193557541City Hotel12502016September40261320.00BBPRTOffline TA/TOTA/TO000AA0Non Refund3.0NaN0Transient95.0000Canceled28-06-16
1193682770City Hotel1682016February8170220.00BBPRTGroupsTA/TO010AA0Non Refund37.0NaN0Transient75.0000Canceled06-01-16
11937109861City Hotel01772017April15152120.00HBCNGroupsTA/TO000AA1No Deposit2.0NaN0Transient-Party105.3301Check-Out18-04-17
1193834515Resort Hotel02412017March12212510.00BBGBRGroupsTA/TO000AA0No Deposit273.0NaN0Transient-Party28.8000Check-Out28-03-17